Laying the Groundwork for Hypothesis Making in EAP Lecture Comprehension

Native speakers, when listening to lectures, sift through the information to choose what to listen to, make hypotheses about future discourse, synthesize preceding discourse, and add their own background knowledge. Nonnative speakers, too, need to be aware of their active role as listener. They also need to be aware of the fact that their foreign language and foreign culture background may lead them to make predictions and interpret information during an English lecture differently than native English speakers. This article will present relevant theories of discourse processing for native and non-native speakers of English and suggest exercises for non native speakers based on these theories geared towards awareness and improvement of hypothesis making during lectures

Nitrogen limitation of net primary productivity in terrestrial ecosystems is globally distributed.

Our meta-analysis of 126 nitrogen addition experiments evaluated nitrogen (N) limitation of net primary production (NPP) in terrestrial ecosystems. We tested the hypothesis that N limitation is widespread among biomes and influenced by geography and climate. We used the response ratio (R approximately equal ANPP(N)/ANPP(ctrl)) of aboveground plant growth in fertilized to control plots and found that most ecosystems are nitrogen limited with an average 29% growth response to nitrogen (i.e., R = 1.29). The response ratio was significant within temperate forests (R = 1.19), tropical forests (R = 1.60), temperate grasslands (R = 1.53), tropical grasslands (R = 1.26), wetlands (R = 1.16), and tundra (R = 1.35), but not deserts. Eight tropical forest studies had been conducted on very young volcanic soils in Hawaii, and this subgroup was strongly N limited (R = 2.13), which resulted in a negative correlation between forest R and latitude. The degree of N limitation in the remainder of the tropical forest studies (R = 1.20) was comparable to that of temperate forests, and when the young Hawaiian subgroup was excluded, forest R did not vary with latitude. Grassland response increased with latitude, but was independent of temperature and precipitation. These results suggest that the global N and C cycles interact strongly and that geography can mediate ecosystem response to N within certain biome types

A Syllabus for an Advanced ESL Lecture Comprehension and Note-taking Course

This thesis presents a semester-long syllabus with sample materials for a lecture comprehension and note-taking class for advanced ESL students in a university setting. The syllabus presupposes a high level of grammatical competence on the part of the students, taking for granted that it is not on the level of lexical or sentential comprehension that the student has difficulty. Rather, problems are assumed to stem from insufficient time of processing due to lack of familiarity with the language and the assumptions concerning lecture discourse in that language. Background information is cited regarding research in connected discourse processing, the effect of culture on that processing, lecture discourse analyses, and lecture comprehension and note-taking pedagogy and skill needs. A needs analysis concerning the listening comprehension, note-taking, and production requirements of university students is presented and discussed.This thesis presents a semester-long syllabus with sample materials for a lecture comprehension and note-taking class for advanced ESL students in a university setting. The syllabus presupposes a high level of grammatical competence on the part of the students, taking for granted that it is not on the level of lexical or sentential comprehension that the student has difficulty. Rather, problems are assumed to stem from insufficient time of processing due to lack of familiarity with the language and the assumptions concerning lecture discourse in that language. Background information is cited regarding research in connected discourse processing, the effect of culture on that processing, lecture discourse analyses, and lecture comprehension and note-taking pedagogy and skill needs. A needs analysis concerning the listening comprehension, note-taking, and production requirements of university students is presented and discussed

Increasing racial diversity in the North American Plant Phenotyping Network through conference participation support

A key goal of the North American Plant Phenotyping Network (NAPPN) annual conference is to cultivate a new generation of scientists from diverse backgrounds. As part of their effort to diversify the plant phenomics research community, NAPPN acquired funding to cover all attendance costs for participants from historically black colleges and universities (HBCU) for the 2022 annual meeting. Seven award recipients represented the first attendees from HBCUs in the conference’s 6-year history. In this commentary, we report on the impact of the conference awards, including lessons learned, and the future of the award

TERRA-REF Analysis Workbench: Container-based Environments for Low-Barrier Access to Research Data

TERRA-REF involves automated transfer and processing of large volumes of plant sensing data, in order to accelerate the study of genomic and phenomic observations in controlled field experiments. Multiple terabytes per day are moved and various workflows are triggered to derive metadata, traits and genome sequences from raw input formats. The TERRA-REF Analysis Workbench environment allows users to launch analysis environments with preconfigured and customizable software to examine and compute against this very large reference dataset

Ten Simple Rules for Digital Data Storage

Data is the central currency of science, but the nature of scientific data has changed dramatically with the rapid pace of technology. This change has led to the development of a wide variety of data formats, dataset sizes, data complexity, data use cases, and data sharing practices. Improvements in high throughput DNA sequencing, sustained institutional support for large sensor networks, and sky surveys with large-format digital cameras have created massive quantities of data. At the same time, the combination of increasingly diverse research teams and data aggregation in portals (e.g. for biodiversity data, GBIF or iDigBio) necessitates increased coordination among data collectors and institutions. As a consequence, “data” can now mean anything from petabytes of information stored in professionally-maintained databases, through spreadsheets on a single computer, to hand-written tables in lab notebooks on shelves. All remain important, but data curation practices must continue to keep pace with the changes brought about by new forms and practices of data collection and storage.</jats:p

Data-Driven Artificial Intelligence for Calibration of Hyperspectral Big Data

Near-earth hyperspectral big data present both huge opportunities and challenges for spurring developments in agriculture and high-throughput plant phenotyping and breeding. In this article, we present data-driven approaches to address the calibration challenges for utilizing near-earth hyperspectral data for agriculture. A data-driven, fully automated calibration workflow that includes a suite of robust algorithms for radiometric calibration, bidirectional reflectance distribution function (BRDF) correction and reflectance normalization, soil and shadow masking, and image quality assessments was developed. An empirical method that utilizes predetermined models between camera photon counts (digital numbers) and downwelling irradiance measurements for each spectral band was established to perform radiometric calibration. A kernel-driven semiempirical BRDF correction method based on the Ross Thick-Li Sparse (RTLS) model was used to normalize the data for both changes in solar elevation and sensor view angle differences attributed to pixel location within the field of view. Following rigorous radiometric and BRDF corrections, novel rule-based methods were developed to conduct automatic soil removal; and a newly proposed approach was used for image quality assessment; additionally, shadow masking and plot-level feature extraction were carried out. Our results show that the automated calibration, processing, storage, and analysis pipeline developed in this work can effectively handle massive amounts of hyperspectral data and address the urgent challenges related to the production of sustainable bioenergy and food crops, targeting methods to accelerate plant breeding for improving yield and biomass traits

Robust paths to net greenhouse gas mitigation and negative emissions via advanced biofuels

ACKNOWLEDGEMENTS We thank Dennis Ojima and Daniel L. Sanchez for their encouragement on this topic. The authors gratefully acknowledge partial support as follows: J.L.F., L.R.L., T.L.R., E.A.H.S., and J.J.S., the Sao Paulo Research Foundation (FAPESP grant# 2014/26767-9); J.L.F., L.R.L., K.P., and T.L.R., The Center for Bioenergy Innovation, a U.S. Department of Energy Research Center supported by the Office of Biological and Environmental Research in the DOE Office of Science (grant# DE-AC05-00OR22725); L.R.L., the Sao Paulo Research Foundation, and the Link Foundation; J.L.F. and K.P., USDA/NIFA (grant# 2013-68005-21298 and 2017-67019-26327); T.L.R., USDA/NIFA (grant# 2012-68005-19703); D.S.L. and S.P.L., the Energy Biosciences Institute. Data availability The DayCent model (https://www2.nrel.colostate.edu/projects/daycent/) is freely available upon request. Specification of DayCent model runs and automated model initialization, calibration, scenario simulation, results analysis, and figure generation were implemented in Python 2.7, using the numpy module for data processing and the matplotlib module for figure generation. Analysis code is available in a version-controlled repository (https://github.com/johnlfield/Ecosystem_dynamics). A working copy of the code, all associated DayCent model inputs, and analysis outputs are also available in an online data repository (https://figshare.com/s/4c14ec168bd550db4bad; note this URL is for accessing a private version of the repository, and will eventually be replaced with an updated URL for the public version of the repository, which will only be accessible after the journal-specified embargo date).Peer reviewedPostprintPublisher PD

Workshop Report: Container Based Analysis Environments for Research Data Access and Computing

Report of the first workshop on Container Based Analysis Environments for Research Data Access and Computing supported by the National Data Service and Data Exploration Lab and held at the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign

Modelling the carbon cycle of Miscanthus plantations: existing models and the potential for their improvement

The lignocellulosic perennial grass Miscanthus has received considerable attention as a potential bioenergy crop over the last 25 years, but few commercial plantations exist globally. This is partly due to the uncertainty associated with claims that land use change (LUC) to Miscanthus will result in both commercially viable yields and net increases in carbon (C) storage. To simulate what the effects may be after LUC to Miscanthus, six process-based models have been parameterised for Miscanthus and here we review how these models operate. This review provides an overview of the key Miscanthus soil organic matter models and then highlights what measurers can do to accelerate model development. Each model (WIMOVAC, BioCro, Agro-IBIS, DAYCENT, DNDC and ECOSSE) is capable of simulating biomass production and soil C dynamics based on specific site characteristics. Understanding the design of these models is important in model selection as well as being important for field researchers to collect the most relevant data to improve model performance. The rapid increase in models parameterised for Miscanthus is promising but refinements and improvements are still required to ensure model predictions are reliable and can be applied to spatial scales relevant for policy. Specific improvements, needed to ensure the models are applicable for a range of environmental conditions, come under two categories: 1) increased data generation and 2) development of frameworks and databases to allow simulations of ranging scales. Research into non-food bioenergy crops such as Miscanthus is relatively recent and this review highlights that there are still a number of knowledge gaps regarding Miscanthus specifically. For example, the low input requirements of Miscanthus make it particularly attractive as a bioenergy crop but it is essential that we increase our understanding of the crop’s nutrient re-mobilisation and ability to host N-fixing organisms in order to derive the most accurate simulations